In such environments, data collection operations are typically executed by a central processing system and apply to data usually coming from multiple equipments: Process Equipments, Metrology Equipments, External Additional Sensors, Other application Automation/Manufacturing Execution System (MES), Databases, etc.
Due to a continuous technical progress in the field of semiconductors, these equipments are indeed more and more many and/or more and more complex due to the miniaturization of the manufactured components, and require a high degree of reliability for meeting high semiconductor products standards.
Thus it is a need of a control system for monitoring and analyzing the whole data coming from all the equipments to enhance the production quality control. More specifically, in order to avoid such a control system working on bad quality data, and consequently raising false alarms and the like, there is a need for a control of the quality of the data supply chain.
More specifically, when a system for production quality control uses pre-treatments such as a “data windowing” scheme to select only the useful data corresponding to what is requested for further data analysis and therefore optimize the necessary amount of collected data to maintain in the data base, and summarization algorithms to translate the raw data into indicators, corresponding to the desired physical and statistical process information needed for production quality control, these pre-treatments are liable to provide false results due to missing or non-synchronized raw data. In other words, the underlying algorithms are not capable of detecting the origin of the presence of these bad raw data quality.
Therefore, false alarms or the like problems can be generated while the problem arises not from the data values but from the data collection chain.